OpenRaiser/Intern-Atlas huggingface dataset:
Total Order Reliable Convergecast in WBAN
Venue: arXiv cs.NI 2016
Year: 2016

This paper is the first extensive work on total order reliable convergecast in multi-hop Wireless Body Area Networks (WBAN). Convergecast is a many-to-one cooperative scheme where each node of the network transmits data towards the same sink. Our contribution is threefold. First, we stress existing WBAN convergecast strategies with respect to their capacity to be reliable and to ensure the total order delivery at sink. That is, packets sent in a specific order should be received in the same order by the sink. When stressed with transmission rates up to 500 packets per second the performances of these strategies decrease dramatically (more than 90% of packets lost). Secondly, we propose a new posture-centric model for WBAN. This model offers a good characterization of the path availability which is further used to fine tune the retransmission rate thresholds. Third, based on our model we propose a new mechanism for reliability and a new converge-cast strategy that outperforms WBAN dedicated strategies but also strategies adapted from DTN and WSN areas. Our extensive performance evaluations use essential parameters for WBAN: packet lost, total order reliability (messages sent in a specific order should be delivered in that specific order) and various human body postures. In particular, our strategy ensures zero packet order inversions for various transmission rates and mobility postures. Interestingly, our strategy respects this property without the need of additional energy-guzzler mechanisms.
Realtime Hierarchical Clustering based on Boundary and Surface Statistics
Venue: arXiv cs.CV 2016
Year: 2016

Visual grouping is a key mechanism in human scene perception. There, it belongs to the subconscious, early processing and is key prerequisite for other high level tasks such as recognition. In this paper, we introduce an efficient, realtime capable algorithm which likewise agglomerates a valuable hierarchical clustering of a scene, while using purely local appearance statistics. To speed up the processing, first we subdivide the image into meaningful, atomic segments using a fast Watershed transform. Starting from there, our rapid, agglomerative clustering algorithm prunes and maintains the connectivity graph between clusters to contain only such pairs, which directly touch in the image domain and are reciprocal nearest neighbors (RNN) wrt. a distance metric. The core of this approach is our novel cluster distance: it combines boundary and surface statistics both in terms of appearance as well as spatial linkage. This yields state-of-the-art performance, as we demonstrate in conclusive experiments conducted on BSDS500 and Pascal-Context datasets.
Quantum Neural Machine Learning - Backpropagation and Dynamics
Venue: arXiv cs.NE 2016
Year: 2016

The current work addresses quantum machine learning in the context of Quantum Artificial Neural Networks such that the networks' processing is divided in two stages: the learning stage, where the network converges to a specific quantum circuit, and the backpropagation stage where the network effectively works as a self-programing quantum computing system that selects the quantum circuits to solve computing problems. The results are extended to general architectures including recurrent networks that interact with an environment, coupling with it in the neural links' activation order, and self-organizing in a dynamical regime that intermixes patterns of dynamical stochasticity and persistent quasiperiodic dynamics, making emerge a form of noise resilient dynamical record.
The Digital Synaptic Neural Substrate: Size and Quality Matters
Venue: arXiv cs.AI 2016
Year: 2016

We investigate the 'Digital Synaptic Neural Substrate' (DSNS) computational creativity approach further with respect to the size and quality of images that can be used to seed the process. In previous work we demonstrated how combining photographs of people and sequences taken from chess games between weak players can be used to generate chess problems or puzzles of higher aesthetic quality, on average, compared to alternative approaches. In this work we show experimentally that using larger images as opposed to smaller ones improves the output quality even further. The same is also true for using clearer or less corrupted images. The reasons why these things influence the DSNS process is presently not well-understood and debatable but the findings are nevertheless immediately applicable for obtaining better results.
Gridlan: a Multi-purpose Local Grid Computing Framework
Venue: arXiv cs.DC 2016
Year: 2016

In scientific computing, more computational power generally implies faster and possibly more detailed results. The goal of this study was to develop a framework to submit computational jobs to powerful workstations underused by nonintensive tasks. This is achieved by using a virtual machine in each of these workstations, where the computations are done. This group of virtual machines is called the Gridlan. The Gridlan framework is intermediate between the cluster and grid computing paradigms. The Gridlan is able to profit from existing cluster software tools, such as resource managers like Torque, so a user with previous experience in cluster operation can dispatch jobs seamlessly. A benchmark test of the Gridlan implementation shows the system's suitability for computational tasks, principally in embarrassingly parallel computations.
Multi-document abstractive summarization using ILP based multi-sentence compression
Venue: arXiv cs.CL 2016
Year: 2016

Abstractive summarization is an ideal form of summarization since it can synthesize information from multiple documents to create concise informative summaries. In this work, we aim at developing an abstractive summarizer. First, our proposed approach identifies the most important document in the multi-document set. The sentences in the most important document are aligned to sentences in other documents to generate clusters of similar sentences. Second, we generate K-shortest paths from the sentences in each cluster using a word-graph structure. Finally, we select sentences from the set of shortest paths generated from all the clusters employing a novel integer linear programming (ILP) model with the objective of maximizing information content and readability of the final summary. Our ILP model represents the shortest paths as binary variables and considers the length of the path, information score and linguistic quality score in the objective function. Experimental results on the DUC 2004 and 2005 multi-document summarization datasets show that our proposed approach outperforms all the baselines and state-of-the-art extractive summarizers as measured by the ROUGE scores. Our method also outperforms a recent abstractive summarization technique. In manual evaluation, our approach also achieves promising results on informativeness and readability.
Deep Quality: A Deep No-reference Quality Assessment System
Venue: arXiv cs.MM 2016
Year: 2016

Image quality assessment (IQA) continues to garner great interest in the research community, particularly given the tremendous rise in consumer video capture and streaming. Despite significant research effort in IQA in the past few decades, the area of no-reference image quality assessment remains a great challenge and is largely unsolved. In this paper, we propose a novel no-reference image quality assessment system called Deep Quality, which leverages the power of deep learning to model the complex relationship between visual content and the perceived quality. Deep Quality consists of a novel multi-scale deep convolutional neural network, trained to learn to assess image quality based on training samples consisting of different distortions and degradations such as blur, Gaussian noise, and compression artifacts. Preliminary results using the CSIQ benchmark image quality dataset showed that Deep Quality was able to achieve strong quality prediction performance (89% patch-level and 98% image-level prediction accuracy), being able to achieve similar performance as full-reference IQA methods.
Determinability and state estimation for switched differential-algebraic equations
Venue: arXiv cs.SY 2016
Year: 2016

The problem of state reconstruction and estimation is considered for a class of switched dynamical systems whose subsystems are modeled using linear differential-algebraic equations (DAEs). Since this system class imposes time-varying dynamic and static (in the form of algebraic constraints) relations on the evolution of state trajectories, an appropriate notion of observability is presented which accommodates these phenomena. Based on this notion, we first derive a formula for the reconstruction of the state of the system where we explicitly obtain an injective mapping from the output to the state. In practice, such a mapping may be difficult to realize numerically and hence a class of estimators is proposed which ensures that the state estimate converges asymptotically to the real state of the system.
Strong connectivity and its applications
Venue: arXiv cs.DM 2016
Year: 2016

Directed graphs are widely used in modelling of nonsymmetric relations in various sciences and engineering disciplines. We discuss invariants of strongly connected directed graphs - minimal number of vertices or edges necessary to remove to make remaining graphs not strongly connected. By analogy with undirected graphs these invariants are called strong vertex/edge connectivities. We review some properties of these invariants. Computational results for some publicly available connectome graphs used in neuroscience are described.
On the Impact of Wireless Jamming on the Distributed Secondary Microgrid Control
Venue: arXiv cs.SY 2016
Year: 2016

The secondary control in direct current microgrids (MGs) is used to restore the voltage deviations caused by the primary droop control, where the latter is implemented locally in each distributed generator and reacts to load variations. Numerous recent works propose to implement the secondary control in a distributed fashion, relying on a communication system to achieve consensus among MG units. This paper shows that, if the system is not designed to cope with adversary communication impairments, then a malicious attacker can apply a simple jamming of a few units of the MG and thus compromise the secondary MG control. Compared to other denial-of-service attacks that are oriented against the tertiary control, such as economic dispatch, the attack on the secondary control presented here can be more severe, as it disrupts the basic functionality of the MG.
Multi-Output Artificial Neural Network for Storm Surge Prediction in North Carolina
Venue: arXiv cs.NE 2016
Year: 2016

During hurricane seasons, emergency managers and other decision makers need accurate and `on-time' information on potential storm surge impacts. Fully dynamical computer models, such as the ADCIRC tide, storm surge, and wind-wave model take several hours to complete a forecast when configured at high spatial resolution. Additionally, statically meaningful ensembles of high-resolution models (needed for uncertainty estimation) cannot easily be computed in near real-time. This paper discusses an artificial neural network model for storm surge prediction in North Carolina. The network model provides fast, real-time storm surge estimates at coastal locations in North Carolina. The paper studies the performance of the neural network model vs. other models on synthetic and real hurricane data.
Online Identification of Time-Varying Systems: a Bayesian approach
Venue: arXiv cs.SY 2016
Year: 2016

We extend the recently introduced regularization/Bayesian System Identification procedures to the estimation of time-varying systems. Specifically, we consider an online setting, in which new data become available at given time steps. The real-time estimation requirements imposed by this setting are met by estimating the hyper-parameters through just one gradient step in the marginal likelihood maximization and by exploiting the closed-form availability of the impulse response estimate (when Gaussian prior and Gaussian measurement noise are postulated). By relying on the use of a forgetting factor, we propose two methods to tackle the tracking of time-varying systems. In one of them, the forgetting factor is estimated by treating it as a hyper-parameter of the Bayesian inference procedure.
Speaker Recognition for Children's Speech
Venue: arXiv cs.SD 2016
Year: 2016

This paper presents results on Speaker Recognition (SR) for children's speech, using the OGI Kids corpus and GMM-UBM and GMM-SVM SR systems. Regions of the spectrum containing important speaker information for children are identified by conducting SR experiments over 21 frequency bands. As for adults, the spectrum can be split into four regions, with the first (containing primary vocal tract resonance information) and third (corresponding to high frequency speech sounds) being most useful for SR. However, the frequencies at which these regions occur are from 11% to 38% higher for children. It is also noted that subband SR rates are lower for younger children. Finally results are presented of SR experiments to identify a child in a class (30 children, similar age) and school (288 children, varying ages). Class performance depends on age, with accuracy varying from 90% for young children to 99% for older children. The identification rate achieved for a child in a school is 81%.
Derivative Delay Embedding: Online Modeling of Streaming Time Series
Venue: arXiv cs.LG 2016
Year: 2016

The staggering amount of streaming time series coming from the real world calls for more efficient and effective online modeling solution. For time series modeling, most existing works make some unrealistic assumptions such as the input data is of fixed length or well aligned, which requires extra effort on segmentation or normalization of the raw streaming data. Although some literature claim their approaches to be invariant to data length and misalignment, they are too time-consuming to model a streaming time series in an online manner. We propose a novel and more practical online modeling and classification scheme, DDE-MGM, which does not make any assumptions on the time series while maintaining high efficiency and state-of-the-art performance. The derivative delay embedding (DDE) is developed to incrementally transform time series to the embedding space, where the intrinsic characteristics of data is preserved as recursive patterns regardless of the stream length and misalignment. Then, a non-parametric Markov geographic model (MGM) is proposed to both model and classify the pattern in an online manner. Experimental results demonstrate the effectiveness and superior classification accuracy of the proposed DDE-MGM in an online setting as compared to the state-of-the-art.
Proving Linearizability via Branching Bisimulation
Venue: arXiv cs.PL 2016
Year: 2016

Linearizability and progress properties are key correctness notions for concurrent objects. However, model checking linearizability has suffered from the PSPACE-hardness of the trace inclusion problem. This paper proposes to exploit branching bisimulation, a fundamental semantic equivalence relation developed for process algebras which can be computed efficiently, in checking these properties. A quotient construction is provided which results in huge state space reductions. We confirm the advantages of the proposed approach on more than a dozen benchmark problems.
A Character-level Convolutional Neural Network for Distinguishing Similar Languages and Dialects
Venue: arXiv cs.CL 2016
Year: 2016

Discriminating between closely-related language varieties is considered a challenging and important task. This paper describes our submission to the DSL 2016 shared-task, which included two sub-tasks: one on discriminating similar languages and one on identifying Arabic dialects. We developed a character-level neural network for this task. Given a sequence of characters, our model embeds each character in vector space, runs the sequence through multiple convolutions with different filter widths, and pools the convolutional representations to obtain a hidden vector representation of the text that is used for predicting the language or dialect. We primarily focused on the Arabic dialect identification task and obtained an F1 score of 0.4834, ranking 6th out of 18 participants. We also analyze errors made by our system on the Arabic data in some detail, and point to challenges such an approach is faced with.
An Investigation of Recurrent Neural Architectures for Drug Name Recognition
Venue: arXiv cs.CL 2016
Year: 2016

Drug name recognition (DNR) is an essential step in the Pharmacovigilance (PV) pipeline. DNR aims to find drug name mentions in unstructured biomedical texts and classify them into predefined categories. State-of-the-art DNR approaches heavily rely on hand crafted features and domain specific resources which are difficult to collect and tune. For this reason, this paper investigates the effectiveness of contemporary recurrent neural architectures - the Elman and Jordan networks and the bidirectional LSTM with CRF decoding - at performing DNR straight from the text. The experimental results achieved on the authoritative SemEval-2013 Task 9.1 benchmarks show that the bidirectional LSTM-CRF ranks closely to highly-dedicated, hand-crafted systems.
DimensionApp : android app to estimate object dimensions
Venue: arXiv cs.CV 2016
Year: 2016

In this project, we develop an android app that uses on computer vision techniques to estimate an object dimension present in field of view. The app while having compact size, is accurate upto +/- 5 mm and robust towards touch inputs. We use single-view metrology to compute accurate measurement. Unlike previous approaches, our technique does not rely on line detection and can be generalize to any object shape easily.
From Digital computers to quantum computers based on biological paradigms and progress in particle physics
Venue: arXiv cs.ET 2016
Year: 2016

While several paths have emerged in microelectronics and computing as follow-ons to Turing architectures, and have been implemented using essentially silicon circuits, very little beyond Moore research has considered: (1) first biological processes instead of sequential instructions, and, (2) the implementation of these processes exploiting particle physics interactions. This last combination enables native spatial-temporal integration and correlation, but also powerful interference filtering, gating, splitting and more. These biological functions, their realization by quantum and charge carrier interactions, allow proposing a novel computing architecture, with interfaces, information storage, and programmability. The paper presents the underlying biological processes, the particle physics phenomena which are exploited, and the proposed architecture, as well as an algebraic design formalism.
Popularity in the generalized Hospital Residents Setting
Venue: arXiv cs.DS 2016
Year: 2016

We consider the problem of computing popular matchings in a bipartite graph G = (R U H, E) where R and H denote a set of residents and a set of hospitals respectively. Each hospital h has a positive capacity denoting the number of residents that can be matched to h. The residents and the hospitals specify strict preferences over each other. This is the well-studied Hospital Residents (HR) problem which is a generalization of the Stable Marriage (SM) problem. The goal is to assign residents to hospitals optimally while respecting the capacities of the hospitals. Stability is a well-accepted notion of optimality in such problems. However, motivated by the need for larger cardinality matchings, alternative notions of optimality like popularity have been investigated in the SM setting. In this paper, we consider a generalized HR setting -- namely the Laminar Classified Stable Matchings (LCSM+) problem. Here, additionally, hospitals can specify classifications over residents in their preference lists and classes have upper quotas. We show the following new results: We define a notion of popularity and give a structural characterization of popular matchings for the LCSM+ problem. Assume n = |R| + |H| and m = |E|. We give an O(mn) time algorithm for computing a maximum cardinality popular matching in an LCSM+ instance. We give an O(mn^2) time algorithm for computing a matching that is popular amongst the maximum cardinality matchings in an LCSM+ instance.
Simultaneous Spectrum Sensing and Data Reception for Cognitive Spatial Multiplexing Distributed Systems
Venue: arXiv cs.IT 2016
Year: 2016

A multi-user cognitive (secondary) radio system is considered, where the spatial multiplexing mode of operation is implemented amongst the nodes, under the presence of multiple primary transmissions. The secondary receiver carries out minimum mean-squared error (MMSE) detection to effectively decode the secondary data streams, while it performs spectrum sensing at the remaining signal to capture the presence of primary activity or not. New analytical closed-form expressions regarding some important system measures are obtained, namely, the outage and detection probabilities; the transmission power of the secondary nodes; the probability of unexpected interference at the primary nodes; {\color{blue}and the detection efficiency with the aid of the area under the receive operating characteristics curve}. The realistic scenarios of channel fading time variation and channel estimation errors are encountered for the derived results. Finally, the enclosed numerical results verify the accuracy of the proposed framework, while some useful engineering insights are also revealed, such as the key role of the detection accuracy to the overall performance and the impact of transmission power from the secondary nodes to the primary system.
The Dating Problem and Optimal Ordering of Sequential Opportunities
Venue: arXiv cs.GT 2016
Year: 2016

In this paper, we discuss a stochastic decision problem of optimally selecting the order in which to try $n$ opportunities that may yield an uncertain reward in the future. The motivation came out from pure curiosity, after an informal conversation about what could be the best way to date friends. The problem structure turned out to be suitable also for other situations, such as the problem of optimally selecting the order of submission of a paper to journals. Despite the seemingly combinatorial nature of the problem, we show that optimal-tradeoff solutions can be found by simply ordering a sequence of $n$ real numbers.
Fractality of Massive Graphs: Scalable Analysis with Sketch-Based Box-Covering Algorithm
Venue: arXiv cs.DS 2016
Year: 2016

Analysis and modeling of networked objects are fundamental pieces of modern data mining. Most real-world networks, from biological to social ones, are known to have common structural properties. These properties allow us to model the growth processes of networks and to develop useful algorithms. One remarkable example is the fractality of networks, which suggests the self-similar organization of global network structure. To determine the fractality of a network, we need to solve the so-called box-covering problem, where preceding algorithms are not feasible for large-scale networks. The lack of an efficient algorithm prevents us from investigating the fractal nature of large-scale networks. To overcome this issue, we propose a new box-covering algorithm based on recently emerging sketching techniques. We theoretically show that it works in near-linear time with a guarantee of solution accuracy. In experiments, we have confirmed that the algorithm enables us to study the fractality of million-scale networks for the first time. We have observed that its outputs are sufficiently accurate and that its time and space requirements are orders of magnitude smaller than those of previous algorithms.